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Unverified Commit 78c29c35 authored by Ziyi Wu's avatar Ziyi Wu Committed by GitHub
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[Enhance] Add pipeline for data loading (#430) 

* reuse pipeline in scannet-det dataset

* reuse pipeline in kitti dataset

* reuse pipeline in lyft dataset

* reuse pipeline in sunrgbd dataset

* reuse pipeline in nuscenes dataset

* reuse pipeline in waymo dataset

* reuse pipeline in scannet-seg dataset

* add eval_pipeline in configs which have custom data pipelines

* move data loading via pipeline to dataset._extract_data() for clarity

* use eval_pipeline in tools/misc/visualize_results.py

* get_pipeline from self when no pipeline is provided

* fix small bugs

* fix small bugs

* simplify and clear code

* remove unnecessary eval_pipeline added

* add comment about why we set self.test_mode=False

* small fix

* modify docs about config
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configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py
View file @ 78c29c35
...@@ -260,6 +260,16 @@ test_pipeline = [ ...@@ -260,6 +260,16 @@ test_pipeline = [
dict(type='Collect3D', keys=['points']) dict(type='Collect3D', keys=['points'])
]) ])
] ]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict( data = dict(
samples_per_gpu=2, samples_per_gpu=2,
...@@ -301,4 +311,5 @@ data = dict( ...@@ -301,4 +311,5 @@ data = dict(
# Part-A2 uses a different learning rate from what SECOND uses. # Part-A2 uses a different learning rate from what SECOND uses.
lr = 0.001 lr = 0.001
optimizer = dict(lr=lr) optimizer = dict(lr=lr)
evaluation = dict(pipeline=eval_pipeline)
find_unused_parameters = True find_unused_parameters = True
docs/tutorials/config.md
View file @ 78c29c35
...@@ -242,6 +242,22 @@ test_pipeline = [ # Testing pipeline, refer to mmdet3d.datasets.pipelines for m ...@@ -242,6 +242,22 @@ test_pipeline = [ # Testing pipeline, refer to mmdet3d.datasets.pipelines for m
dict(type='Collect3D', # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details dict(type='Collect3D', # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details
keys=['points']) keys=['points'])
] ]
eval_pipeline = [ # Pipeline used for evaluation or visualization, refer to mmdet3d.datasets.pipelines for more details
dict(
type='LoadPointsFromFile', # First pipeline to load points, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
shift_height=True, # Whether to use shifted height
load_dim=6, # The dimension of the loaded points
use_dim=[0, 1, 2]), # Which dimensions of the points to be used
dict(
type='DefaultFormatBundle3D', # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formating for more details
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
with_label=False),
dict(type='Collect3D', # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details
keys=['points'])
]
data = dict( data = dict(
samples_per_gpu=8, # Batch size of a single GPU samples_per_gpu=8, # Batch size of a single GPU
workers_per_gpu=4, # Worker to pre-fetch data for each single GPU workers_per_gpu=4, # Worker to pre-fetch data for each single GPU
...@@ -347,6 +363,22 @@ data = dict( ...@@ -347,6 +363,22 @@ data = dict(
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin'), # Names of classes 'garbagebin'), # Names of classes
test_mode=True)) # Whether to use test mode test_mode=True)) # Whether to use test mode
evaluation = dict(pipeline=[ # Pipeline is passed by eval_pipeline created before
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin'),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
lr = 0.008 # Learning rate of optimizers lr = 0.008 # Learning rate of optimizers
optimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch optimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
type='Adam', # Type of optimizers, # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details type='Adam', # Type of optimizers, # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details
......
mmdet3d/datasets/custom_3d.py
View file @ 78c29c35
import mmcv import mmcv
import numpy as np import numpy as np
import tempfile import tempfile
import warnings
from os import path as osp from os import path as osp
from torch.utils.data import Dataset from torch.utils.data import Dataset
from mmdet.datasets import DATASETS from mmdet.datasets import DATASETS
from ..core.bbox import get_box_type from ..core.bbox import get_box_type
from .pipelines import Compose from .pipelines import Compose
from .utils import get_loading_pipeline
@DATASETS.register_module() @DATASETS.register_module()
...@@ -226,7 +228,8 @@ class Custom3DDataset(Dataset): ...@@ -226,7 +228,8 @@ class Custom3DDataset(Dataset):
iou_thr=(0.25, 0.5), iou_thr=(0.25, 0.5),
logger=None, logger=None,
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluate. """Evaluate.
Evaluation in indoor protocol. Evaluation in indoor protocol.
...@@ -239,6 +242,8 @@ class Custom3DDataset(Dataset): ...@@ -239,6 +242,8 @@ class Custom3DDataset(Dataset):
Default: False. Default: False.
out_dir (str): Path to save the visualization results. out_dir (str): Path to save the visualization results.
Default: None. Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict: Evaluation results. dict: Evaluation results.
...@@ -262,10 +267,88 @@ class Custom3DDataset(Dataset): ...@@ -262,10 +267,88 @@ class Custom3DDataset(Dataset):
box_type_3d=self.box_type_3d, box_type_3d=self.box_type_3d,
box_mode_3d=self.box_mode_3d) box_mode_3d=self.box_mode_3d)
if show: if show:
self.show(results, out_dir) self.show(results, out_dir, pipeline=pipeline)
return ret_dict return ret_dict
def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
raise NotImplementedError('_build_default_pipeline is not implemented '
f'for dataset {self.__class__.__name__}')
def _get_pipeline(self, pipeline):
"""Get data loading pipeline in self.show/evaluate function.
Args:
pipeline (list[dict] | None): Input pipeline. If None is given, \
get from self.pipeline.
"""
if pipeline is None:
if not hasattr(self, 'pipeline') or self.pipeline is None:
warnings.warn(
'Use default pipeline for data loading, this may cause '
'errors when data is on ceph')
return self._build_default_pipeline()
loading_pipeline = get_loading_pipeline(self.pipeline.transforms)
return Compose(loading_pipeline)
return Compose(pipeline)
@staticmethod
def _get_data(results, key):
"""Extract and return the data corresponding to key in result dict.
Args:
results (dict): Data loaded using pipeline.
key (str): Key of the desired data.
Returns:
np.ndarray | torch.Tensor | None: Data term.
"""
if key not in results.keys():
return None
# results[key] may be data or list[data]
# data may be wrapped inside DataContainer
data = results[key]
if isinstance(data, list) or isinstance(data, tuple):
data = data[0]
if isinstance(data, mmcv.parallel.DataContainer):
data = data._data
return data
def _extract_data(self, index, pipeline, key, load_annos=False):
"""Load data using input pipeline and extract data according to key.
Args:
index (int): Index for accessing the target data.
pipeline (:obj:`Compose`): Composed data loading pipeline.
key (str | list[str]): One single or a list of data key.
load_annos (bool): Whether to load data annotations.
If True, need to set self.test_mode as False before loading.
Returns:
np.ndarray | torch.Tensor | list[np.ndarray | torch.Tensor]:
A single or a list of loaded data.
"""
assert pipeline is not None, 'data loading pipeline is not provided'
# when we want to load ground-truth via pipeline (e.g. bbox, seg mask)
# we need to set self.test_mode as False so that we have 'annos'
if load_annos:
original_test_mode = self.test_mode
self.test_mode = False
input_dict = self.get_data_info(index)
self.pre_pipeline(input_dict)
example = pipeline(input_dict)
# extract data items according to keys
if isinstance(key, str):
data = self._get_data(example, key)
else:
data = [self._get_data(example, k) for k in key]
if load_annos:
self.test_mode = original_test_mode
return data
def __len__(self): def __len__(self):
"""Return the length of data infos. """Return the length of data infos.
......
mmdet3d/datasets/custom_3d_seg.py
View file @ 78c29c35
import mmcv import mmcv
import numpy as np import numpy as np
import tempfile import tempfile
import torch import warnings
from os import path as osp from os import path as osp
from torch.utils.data import Dataset from torch.utils.data import Dataset
from mmdet.datasets import DATASETS from mmdet.datasets import DATASETS
from .pipelines import Compose from .pipelines import Compose
from .utils import get_loading_pipeline
@DATASETS.register_module() @DATASETS.register_module()
...@@ -308,27 +309,13 @@ class Custom3DSegDataset(Dataset): ...@@ -308,27 +309,13 @@ class Custom3DSegDataset(Dataset):
mmcv.dump(outputs, out) mmcv.dump(outputs, out)
return outputs, tmp_dir return outputs, tmp_dir
def convert_to_label(self, mask):
"""Convert class_id in segmentation mask to label."""
# TODO: currently only support loading from local
# TODO: may need to consider ceph data storage in the future
if isinstance(mask, str):
if mask.endswith('npy'):
mask = np.load(mask)
else:
mask = np.fromfile(mask, dtype=np.long)
mask_copy = mask.copy()
for class_id, label in self.label_map.items():
mask_copy[mask == class_id] = label
return mask_copy
def evaluate(self, def evaluate(self,
results, results,
metric=None, metric=None,
logger=None, logger=None,
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluate. """Evaluate.
Evaluation in semantic segmentation protocol. Evaluation in semantic segmentation protocol.
...@@ -342,6 +329,8 @@ class Custom3DSegDataset(Dataset): ...@@ -342,6 +329,8 @@ class Custom3DSegDataset(Dataset):
Defaults to False. Defaults to False.
out_dir (str, optional): Path to save the visualization results. out_dir (str, optional): Path to save the visualization results.
Defaults to None. Defaults to None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict: Evaluation results. dict: Evaluation results.
...@@ -354,13 +343,13 @@ class Custom3DSegDataset(Dataset): ...@@ -354,13 +343,13 @@ class Custom3DSegDataset(Dataset):
assert isinstance( assert isinstance(
results[0], dict results[0], dict
), f'Expect elements in results to be dict, got {type(results[0])}.' ), f'Expect elements in results to be dict, got {type(results[0])}.'
load_pipeline = self._get_pipeline(pipeline)
pred_sem_masks = [result['semantic_mask'] for result in results] pred_sem_masks = [result['semantic_mask'] for result in results]
gt_sem_masks = [ gt_sem_masks = [
torch.from_numpy( self._extract_data(
self.convert_to_label( i, load_pipeline, 'pts_semantic_mask', load_annos=True)
osp.join(self.data_root, for i in range(len(self.data_infos))
data_info['pts_semantic_mask_path'])))
for data_info in self.data_infos
] ]
ret_dict = seg_eval( ret_dict = seg_eval(
gt_sem_masks, gt_sem_masks,
...@@ -368,8 +357,9 @@ class Custom3DSegDataset(Dataset): ...@@ -368,8 +357,9 @@ class Custom3DSegDataset(Dataset):
self.label2cat, self.label2cat,
self.ignore_index, self.ignore_index,
logger=logger) logger=logger)
if show: if show:
self.show(pred_sem_masks, out_dir) self.show(pred_sem_masks, out_dir, pipeline=pipeline)
return ret_dict return ret_dict
...@@ -382,6 +372,84 @@ class Custom3DSegDataset(Dataset): ...@@ -382,6 +372,84 @@ class Custom3DSegDataset(Dataset):
pool = np.where(self.flag == self.flag[idx])[0] pool = np.where(self.flag == self.flag[idx])[0]
return np.random.choice(pool) return np.random.choice(pool)
def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
raise NotImplementedError('_build_default_pipeline is not implemented '
f'for dataset {self.__class__.__name__}')
def _get_pipeline(self, pipeline):
"""Get data loading pipeline in self.show/evaluate function.
Args:
pipeline (list[dict] | None): Input pipeline. If None is given, \
get from self.pipeline.
"""
if pipeline is None:
if not hasattr(self, 'pipeline') or self.pipeline is None:
warnings.warn(
'Use default pipeline for data loading, this may cause '
'errors when data is on ceph')
return self._build_default_pipeline()
loading_pipeline = get_loading_pipeline(self.pipeline.transforms)
return Compose(loading_pipeline)
return Compose(pipeline)
@staticmethod
def _get_data(results, key):
"""Extract and return the data corresponding to key in result dict.
Args:
results (dict): Data loaded using pipeline.
key (str): Key of the desired data.
Returns:
np.ndarray | torch.Tensor | None: Data term.
"""
if key not in results.keys():
return None
# results[key] may be data or list[data]
# data may be wrapped inside DataContainer
data = results[key]
if isinstance(data, list) or isinstance(data, tuple):
data = data[0]
if isinstance(data, mmcv.parallel.DataContainer):
data = data._data
return data
def _extract_data(self, index, pipeline, key, load_annos=False):
"""Load data using input pipeline and extract data according to key.
Args:
index (int): Index for accessing the target data.
pipeline (:obj:`Compose`): Composed data loading pipeline.
key (str | list[str]): One single or a list of data key.
load_annos (bool): Whether to load data annotations.
If True, need to set self.test_mode as False before loading.
Returns:
np.ndarray | torch.Tensor | list[np.ndarray | torch.Tensor]:
A single or a list of loaded data.
"""
assert pipeline is not None, 'data loading pipeline is not provided'
# when we want to load ground-truth via pipeline (e.g. bbox, seg mask)
# we need to set self.test_mode as False so that we have 'annos'
if load_annos:
original_test_mode = self.test_mode
self.test_mode = False
input_dict = self.get_data_info(index)
self.pre_pipeline(input_dict)
example = pipeline(input_dict)
# extract data items according to keys
if isinstance(key, str):
data = self._get_data(example, key)
else:
data = [self._get_data(example, k) for k in key]
if load_annos:
self.test_mode = original_test_mode
return data
def __len__(self): def __len__(self):
"""Return the length of scene_idxs. """Return the length of scene_idxs.
......
mmdet3d/datasets/kitti_dataset.py
View file @ 78c29c35
...@@ -12,6 +12,7 @@ from ..core import show_multi_modality_result, show_result ...@@ -12,6 +12,7 @@ from ..core import show_multi_modality_result, show_result
from ..core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode, from ..core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
LiDARInstance3DBoxes, points_cam2img) LiDARInstance3DBoxes, points_cam2img)
from .custom_3d import Custom3DDataset from .custom_3d import Custom3DDataset
from .pipelines import Compose
@DATASETS.register_module() @DATASETS.register_module()
...@@ -300,7 +301,8 @@ class KittiDataset(Custom3DDataset): ...@@ -300,7 +301,8 @@ class KittiDataset(Custom3DDataset):
pklfile_prefix=None, pklfile_prefix=None,
submission_prefix=None, submission_prefix=None,
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluation in KITTI protocol. """Evaluation in KITTI protocol.
Args: Args:
...@@ -317,6 +319,8 @@ class KittiDataset(Custom3DDataset): ...@@ -317,6 +319,8 @@ class KittiDataset(Custom3DDataset):
Default: False. Default: False.
out_dir (str): Path to save the visualization results. out_dir (str): Path to save the visualization results.
Default: None. Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict[str, float]: Results of each evaluation metric. dict[str, float]: Results of each evaluation metric.
...@@ -354,7 +358,7 @@ class KittiDataset(Custom3DDataset): ...@@ -354,7 +358,7 @@ class KittiDataset(Custom3DDataset):
if tmp_dir is not None: if tmp_dir is not None:
tmp_dir.cleanup() tmp_dir.cleanup()
if show: if show:
self.show(results, out_dir) self.show(results, out_dir, pipeline=pipeline)
return ap_dict return ap_dict
def bbox2result_kitti(self, def bbox2result_kitti(self,
...@@ -668,22 +672,47 @@ class KittiDataset(Custom3DDataset): ...@@ -668,22 +672,47 @@ class KittiDataset(Custom3DDataset):
label_preds=np.zeros([0, 4]), label_preds=np.zeros([0, 4]),
sample_idx=sample_idx) sample_idx=sample_idx)
def show(self, results, out_dir, show=True): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
if self.modality['use_camera']:
pipeline.insert(0, dict(type='LoadImageFromFile'))
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online. show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.' assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
example = self.prepare_test_data(i) if 'pts_bbox' in result.keys():
result = result['pts_bbox']
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['point_cloud']['velodyne_path'] pts_path = data_info['point_cloud']['velodyne_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
points, img_metas, img = self._extract_data(
i, pipeline, ['points', 'img_metas', 'img'])
points = points.numpy()
# for now we convert points into depth mode # for now we convert points into depth mode
points = example['points'][0]._data.numpy()
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR, points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH) Coord3DMode.DEPTH)
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy() gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
...@@ -696,9 +725,10 @@ class KittiDataset(Custom3DDataset): ...@@ -696,9 +725,10 @@ class KittiDataset(Custom3DDataset):
file_name, show) file_name, show)
# multi-modality visualization # multi-modality visualization
if self.modality['use_camera'] and \ if self.modality['use_camera'] and 'lidar2img' in img_metas.keys():
'lidar2img' in example['img_metas'][0]._data.keys(): img = img.numpy()
img = mmcv.imread(example['img_metas'][0]._data['filename']) # need to transpose channel to first dim
img = img.transpose(1, 2, 0)
show_pred_bboxes = LiDARInstance3DBoxes( show_pred_bboxes = LiDARInstance3DBoxes(
pred_bboxes, origin=(0.5, 0.5, 0)) pred_bboxes, origin=(0.5, 0.5, 0))
show_gt_bboxes = LiDARInstance3DBoxes( show_gt_bboxes = LiDARInstance3DBoxes(
...@@ -707,7 +737,7 @@ class KittiDataset(Custom3DDataset): ...@@ -707,7 +737,7 @@ class KittiDataset(Custom3DDataset):
img, img,
show_gt_bboxes, show_gt_bboxes,
show_pred_bboxes, show_pred_bboxes,
example['img_metas'][0]._data['lidar2img'], img_metas['lidar2img'],
out_dir, out_dir,
file_name, file_name,
show=False) show=False)
mmdet3d/datasets/lyft_dataset.py
View file @ 78c29c35
...@@ -12,6 +12,7 @@ from mmdet.datasets import DATASETS ...@@ -12,6 +12,7 @@ from mmdet.datasets import DATASETS
from ..core import show_result from ..core import show_result
from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
from .custom_3d import Custom3DDataset from .custom_3d import Custom3DDataset
from .pipelines import Compose
@DATASETS.register_module() @DATASETS.register_module()
...@@ -356,7 +357,8 @@ class LyftDataset(Custom3DDataset): ...@@ -356,7 +357,8 @@ class LyftDataset(Custom3DDataset):
csv_savepath=None, csv_savepath=None,
result_names=['pts_bbox'], result_names=['pts_bbox'],
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluation in Lyft protocol. """Evaluation in Lyft protocol.
Args: Args:
...@@ -375,6 +377,8 @@ class LyftDataset(Custom3DDataset): ...@@ -375,6 +377,8 @@ class LyftDataset(Custom3DDataset):
Default: False. Default: False.
out_dir (str): Path to save the visualization results. out_dir (str): Path to save the visualization results.
Default: None. Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict[str, float]: Evaluation results. dict[str, float]: Evaluation results.
...@@ -395,33 +399,60 @@ class LyftDataset(Custom3DDataset): ...@@ -395,33 +399,60 @@ class LyftDataset(Custom3DDataset):
tmp_dir.cleanup() tmp_dir.cleanup()
if show: if show:
self.show(results, out_dir) self.show(results, out_dir, pipeline=pipeline)
return results_dict return results_dict
def show(self, results, out_dir): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
example = self.prepare_test_data(i) if 'pts_bbox' in result.keys():
points = example['points'][0]._data.numpy() result = result['pts_bbox']
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['lidar_path'] pts_path = data_info['lidar_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
# for now we convert points into depth mode points = self._extract_data(i, pipeline, 'points').numpy()
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR, points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH) Coord3DMode.DEPTH)
inds = result['pts_bbox']['scores_3d'] > 0.1 inds = result['scores_3d'] > 0.1
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy() gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR, show_gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH) Box3DMode.DEPTH)
pred_bboxes = result['pts_bbox']['boxes_3d'][inds].tensor.numpy() pred_bboxes = result['boxes_3d'][inds].tensor.numpy()
pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR, show_pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH) Box3DMode.DEPTH)
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name) show_result(points, show_gt_bboxes, show_pred_bboxes, out_dir,
file_name, show)
def json2csv(self, json_path, csv_savepath): def json2csv(self, json_path, csv_savepath):
"""Convert the json file to csv format for submission. """Convert the json file to csv format for submission.
......
mmdet3d/datasets/nuscenes_dataset.py
View file @ 78c29c35
...@@ -9,6 +9,7 @@ from mmdet.datasets import DATASETS ...@@ -9,6 +9,7 @@ from mmdet.datasets import DATASETS
from ..core import show_result from ..core import show_result
from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
from .custom_3d import Custom3DDataset from .custom_3d import Custom3DDataset
from .pipelines import Compose
@DATASETS.register_module() @DATASETS.register_module()
...@@ -453,7 +454,8 @@ class NuScenesDataset(Custom3DDataset): ...@@ -453,7 +454,8 @@ class NuScenesDataset(Custom3DDataset):
jsonfile_prefix=None, jsonfile_prefix=None,
result_names=['pts_bbox'], result_names=['pts_bbox'],
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluation in nuScenes protocol. """Evaluation in nuScenes protocol.
Args: Args:
...@@ -468,6 +470,8 @@ class NuScenesDataset(Custom3DDataset): ...@@ -468,6 +470,8 @@ class NuScenesDataset(Custom3DDataset):
Default: False. Default: False.
out_dir (str): Path to save the visualization results. out_dir (str): Path to save the visualization results.
Default: None. Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict[str, float]: Results of each evaluation metric. dict[str, float]: Results of each evaluation metric.
...@@ -487,33 +491,61 @@ class NuScenesDataset(Custom3DDataset): ...@@ -487,33 +491,61 @@ class NuScenesDataset(Custom3DDataset):
tmp_dir.cleanup() tmp_dir.cleanup()
if show: if show:
self.show(results, out_dir) self.show(results, out_dir, pipeline=pipeline)
return results_dict return results_dict
def show(self, results, out_dir): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
example = self.prepare_test_data(i) if 'pts_bbox' in result.keys():
points = example['points'][0]._data.numpy() result = result['pts_bbox']
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['lidar_path'] pts_path = data_info['lidar_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
points = self._extract_data(i, pipeline, 'points').numpy()
# for now we convert points into depth mode # for now we convert points into depth mode
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR, points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH) Coord3DMode.DEPTH)
inds = result['pts_bbox']['scores_3d'] > 0.1 inds = result['scores_3d'] > 0.1
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy() gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR, show_gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH) Box3DMode.DEPTH)
pred_bboxes = result['pts_bbox']['boxes_3d'][inds].tensor.numpy() pred_bboxes = result['boxes_3d'][inds].tensor.numpy()
pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR, show_pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH) Box3DMode.DEPTH)
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name) show_result(points, show_gt_bboxes, show_pred_bboxes, out_dir,
file_name, show)
def output_to_nusc_box(detection): def output_to_nusc_box(detection):
......
mmdet3d/datasets/pipelines/__init__.py
View file @ 78c29c35
from mmdet.datasets.pipelines import Compose from mmdet.datasets.pipelines import Compose
from .dbsampler import DataBaseSampler from .dbsampler import DataBaseSampler
from .formating import Collect3D, DefaultFormatBundle, DefaultFormatBundle3D from .formating import Collect3D, DefaultFormatBundle, DefaultFormatBundle3D
from .loading import (LoadAnnotations3D, LoadMultiViewImageFromFiles, from .loading import (LoadAnnotations3D, LoadImageFromFileMono3D,
LoadPointsFromFile, LoadPointsFromMultiSweeps, LoadMultiViewImageFromFiles, LoadPointsFromFile,
NormalizePointsColor, PointSegClassMapping) LoadPointsFromMultiSweeps, NormalizePointsColor,
PointSegClassMapping)
from .test_time_aug import MultiScaleFlipAug3D from .test_time_aug import MultiScaleFlipAug3D
from .transforms_3d import (BackgroundPointsFilter, GlobalRotScaleTrans, from .transforms_3d import (BackgroundPointsFilter, GlobalRotScaleTrans,
IndoorPatchPointSample, IndoorPointSample, IndoorPatchPointSample, IndoorPointSample,
...@@ -19,5 +20,5 @@ __all__ = [ ...@@ -19,5 +20,5 @@ __all__ = [
'NormalizePointsColor', 'LoadAnnotations3D', 'IndoorPointSample', 'NormalizePointsColor', 'LoadAnnotations3D', 'IndoorPointSample',
'PointSegClassMapping', 'MultiScaleFlipAug3D', 'LoadPointsFromMultiSweeps', 'PointSegClassMapping', 'MultiScaleFlipAug3D', 'LoadPointsFromMultiSweeps',
'BackgroundPointsFilter', 'VoxelBasedPointSampler', 'BackgroundPointsFilter', 'VoxelBasedPointSampler',
'IndoorPatchPointSample' 'IndoorPatchPointSample', 'LoadImageFromFileMono3D'
] ]
mmdet3d/datasets/scannet_dataset.py
View file @ 78c29c35
...@@ -7,6 +7,7 @@ from mmdet3d.core.bbox import DepthInstance3DBoxes ...@@ -7,6 +7,7 @@ from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet.datasets import DATASETS from mmdet.datasets import DATASETS
from .custom_3d import Custom3DDataset from .custom_3d import Custom3DDataset
from .custom_3d_seg import Custom3DSegDataset from .custom_3d_seg import Custom3DSegDataset
from .pipelines import Compose
@DATASETS.register_module() @DATASETS.register_module()
...@@ -108,22 +109,40 @@ class ScanNetDataset(Custom3DDataset): ...@@ -108,22 +109,40 @@ class ScanNetDataset(Custom3DDataset):
pts_semantic_mask_path=pts_semantic_mask_path) pts_semantic_mask_path=pts_semantic_mask_path)
return anns_results return anns_results
def show(self, results, out_dir, show=True): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online. show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.' assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['pts_path'] pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
points = np.fromfile( points = self._extract_data(i, pipeline, 'points').numpy()
osp.join(self.data_root, pts_path),
dtype=np.float32).reshape(-1, 6)
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy() gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
pred_bboxes = result['boxes_3d'].tensor.numpy() pred_bboxes = result['boxes_3d'].tensor.numpy()
show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name, show_result(points, gt_bboxes, pred_bboxes, out_dir, file_name,
...@@ -239,25 +258,53 @@ class ScanNetSegDataset(Custom3DSegDataset): ...@@ -239,25 +258,53 @@ class ScanNetSegDataset(Custom3DSegDataset):
anns_results = dict(pts_semantic_mask_path=pts_semantic_mask_path) anns_results = dict(pts_semantic_mask_path=pts_semantic_mask_path)
return anns_results return anns_results
def show(self, results, out_dir, show=True): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16,
24, 28, 33, 34, 36, 39)),
dict(
type='DefaultFormatBundle3D',
with_label=False,
class_names=self.CLASSES),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online. show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.' assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['pts_path'] pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
points = np.fromfile( points, gt_sem_mask = self._extract_data(
osp.join(self.data_root, pts_path), i, pipeline, ['points', 'pts_semantic_mask'], load_annos=True)
dtype=np.float32).reshape(-1, 6) points = points.numpy()
sem_mask_path = data_info['pts_semantic_mask_path']
gt_sem_mask = self.convert_to_label(
osp.join(self.data_root, sem_mask_path))
pred_sem_mask = result['semantic_mask'].numpy() pred_sem_mask = result['semantic_mask'].numpy()
show_seg_result(points, gt_sem_mask, show_seg_result(points, gt_sem_mask,
pred_sem_mask, out_dir, file_name, pred_sem_mask, out_dir, file_name,
......
mmdet3d/datasets/sunrgbd_dataset.py
View file @ 78c29c35
import mmcv
import numpy as np import numpy as np
from collections import OrderedDict from collections import OrderedDict
from os import path as osp from os import path as osp
...@@ -8,6 +7,7 @@ from mmdet3d.core.bbox import DepthInstance3DBoxes ...@@ -8,6 +7,7 @@ from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet.core import eval_map from mmdet.core import eval_map
from mmdet.datasets import DATASETS from mmdet.datasets import DATASETS
from .custom_3d import Custom3DDataset from .custom_3d import Custom3DDataset
from .pipelines import Compose
@DATASETS.register_module() @DATASETS.register_module()
...@@ -152,26 +152,45 @@ class SUNRGBDDataset(Custom3DDataset): ...@@ -152,26 +152,45 @@ class SUNRGBDDataset(Custom3DDataset):
return anns_results return anns_results
def show(self, results, out_dir, show=True): def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
if self.modality['use_camera']:
pipeline.insert(0, dict(type='LoadImageFromFile'))
return Compose(pipeline)
def show(self, results, out_dir, show=True, pipeline=None):
"""Results visualization. """Results visualization.
Args: Args:
results (list[dict]): List of bounding boxes results. results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result. out_dir (str): Output directory of visualization result.
show (bool): Visualize the results online. show (bool): Visualize the results online.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
""" """
assert out_dir is not None, 'Expect out_dir, got none.' assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results): for i, result in enumerate(results):
data_info = self.data_infos[i] data_info = self.data_infos[i]
pts_path = data_info['pts_path'] pts_path = data_info['pts_path']
file_name = osp.split(pts_path)[-1].split('.')[0] file_name = osp.split(pts_path)[-1].split('.')[0]
if hasattr(self, 'pipeline'): points, img_metas, img, calib = self._extract_data(
example = self.prepare_test_data(i) i, pipeline, ['points', 'img_metas', 'img', 'calib'])
else: # scale colors to [0, 255]
example = None points = points.numpy()
points = np.fromfile(
osp.join(self.data_root, pts_path),
dtype=np.float32).reshape(-1, 6)
points[:, 3:] *= 255 points[:, 3:] *= 255
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy() gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
...@@ -180,9 +199,10 @@ class SUNRGBDDataset(Custom3DDataset): ...@@ -180,9 +199,10 @@ class SUNRGBDDataset(Custom3DDataset):
file_name, show) file_name, show)
# multi-modality visualization # multi-modality visualization
if self.modality['use_camera'] and example is not None and \ if self.modality['use_camera'] and 'calib' in data_info.keys():
'calib' in data_info.keys(): img = img.numpy()
img = mmcv.imread(example['img_metas']._data['filename']) # need to transpose channel to first dim
img = img.transpose(1, 2, 0)
pred_bboxes = DepthInstance3DBoxes( pred_bboxes = DepthInstance3DBoxes(
pred_bboxes, origin=(0.5, 0.5, 0)) pred_bboxes, origin=(0.5, 0.5, 0))
gt_bboxes = DepthInstance3DBoxes( gt_bboxes = DepthInstance3DBoxes(
...@@ -191,11 +211,11 @@ class SUNRGBDDataset(Custom3DDataset): ...@@ -191,11 +211,11 @@ class SUNRGBDDataset(Custom3DDataset):
img, img,
gt_bboxes, gt_bboxes,
pred_bboxes, pred_bboxes,
example['calib'], calib,
out_dir, out_dir,
file_name, file_name,
depth_bbox=True, depth_bbox=True,
img_metas=example['img_metas']._data, img_metas=img_metas,
show=show) show=show)
def evaluate(self, def evaluate(self,
...@@ -205,12 +225,31 @@ class SUNRGBDDataset(Custom3DDataset): ...@@ -205,12 +225,31 @@ class SUNRGBDDataset(Custom3DDataset):
iou_thr_2d=(0.5, ), iou_thr_2d=(0.5, ),
logger=None, logger=None,
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluate.
Evaluation in indoor protocol.
Args:
results (list[dict]): List of results.
metric (str | list[str]): Metrics to be evaluated.
iou_thr (list[float]): AP IoU thresholds.
iou_thr_2d (list[float]): AP IoU thresholds for 2d evaluation.
show (bool): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns:
dict: Evaluation results.
"""
# evaluate 3D detection performance # evaluate 3D detection performance
if isinstance(results[0], dict): if isinstance(results[0], dict):
return super().evaluate(results, metric, iou_thr, logger, show, return super().evaluate(results, metric, iou_thr, logger, show,
out_dir) out_dir, pipeline)
# evaluate 2D detection performance # evaluate 2D detection performance
else: else:
eval_results = OrderedDict() eval_results = OrderedDict()
......
mmdet3d/datasets/utils.py
View file @ 78c29c35
# yapf: disable
from mmdet3d.datasets.pipelines import (Collect3D, DefaultFormatBundle3D, from mmdet3d.datasets.pipelines import (Collect3D, DefaultFormatBundle3D,
LoadAnnotations3D, LoadAnnotations3D,
LoadImageFromFileMono3D,
LoadMultiViewImageFromFiles, LoadMultiViewImageFromFiles,
LoadPointsFromFile, LoadPointsFromFile,
LoadPointsFromMultiSweeps) LoadPointsFromMultiSweeps,
MultiScaleFlipAug3D,
PointSegClassMapping)
# yapf: enable
from mmdet.datasets.builder import PIPELINES from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import LoadImageFromFile from mmdet.datasets.pipelines import LoadImageFromFile
def is_loading_function(transform):
"""Judge whether a transform function is a loading function.
Args:
transform (dict | :obj:`Pipeline`): A transform config or a function.
Returns:
bool | None: Whether it is a loading function. None means can't judge.
When transform is `MultiScaleFlipAug3D`, we return None.
"""
# TODO: use more elegant way to distinguish loading modules
loading_functions = (LoadImageFromFile, LoadPointsFromFile,
LoadAnnotations3D, LoadMultiViewImageFromFiles,
LoadPointsFromMultiSweeps, DefaultFormatBundle3D,
Collect3D, LoadImageFromFileMono3D,
PointSegClassMapping)
if isinstance(transform, dict):
obj_cls = PIPELINES.get(transform['type'])
if obj_cls is None:
return False
if obj_cls in loading_functions:
return True
if obj_cls in (MultiScaleFlipAug3D):
return None
elif callable(transform):
if isinstance(transform, loading_functions):
return True
if isinstance(transform, MultiScaleFlipAug3D):
return None
return False
def get_loading_pipeline(pipeline): def get_loading_pipeline(pipeline):
"""Only keep loading image, points and annotations related configuration. """Only keep loading image, points and annotations related configuration.
Args: Args:
pipeline (list[dict]): Data pipeline configs. pipeline (list[dict] | list[:obj:`Pipeline`]):
Data pipeline configs or list of pipeline functions.
Returns: Returns:
list[dict]: The new pipeline list with only keep list[dict] | list[:obj:`Pipeline`]): The new pipeline list with only
loading image, points and annotations related configuration. keep loading image, points and annotations related configuration.
Examples: Examples:
>>> pipelines = [ >>> pipelines = [
...@@ -51,16 +89,19 @@ def get_loading_pipeline(pipeline): ...@@ -51,16 +89,19 @@ def get_loading_pipeline(pipeline):
>>> assert expected_pipelines ==\ >>> assert expected_pipelines ==\
... get_loading_pipeline(pipelines) ... get_loading_pipeline(pipelines)
""" """
loading_pipeline_cfg = [] loading_pipeline = []
for cfg in pipeline: for transform in pipeline:
obj_cls = PIPELINES.get(cfg['type']) is_loading = is_loading_function(transform)
# TODO: use more elegant way to distinguish loading modules if is_loading is None: # MultiScaleFlipAug3D
if obj_cls is not None and obj_cls in ( # extract its inner pipeline
LoadImageFromFile, LoadPointsFromFile, LoadAnnotations3D, if isinstance(transform, dict):
LoadMultiViewImageFromFiles, LoadPointsFromMultiSweeps, inner_pipeline = transform.get('transforms', [])
DefaultFormatBundle3D, Collect3D): else:
loading_pipeline_cfg.append(cfg) inner_pipeline = transform.transforms.transforms
assert len(loading_pipeline_cfg) > 0, \ loading_pipeline.extend(get_loading_pipeline(inner_pipeline))
elif is_loading:
loading_pipeline.append(transform)
assert len(loading_pipeline) > 0, \
'The data pipeline in your config file must include ' \ 'The data pipeline in your config file must include ' \
'loading step.' 'loading step.'
return loading_pipeline_cfg return loading_pipeline
mmdet3d/datasets/waymo_dataset.py
View file @ 78c29c35
...@@ -219,7 +219,8 @@ class WaymoDataset(KittiDataset): ...@@ -219,7 +219,8 @@ class WaymoDataset(KittiDataset):
pklfile_prefix=None, pklfile_prefix=None,
submission_prefix=None, submission_prefix=None,
show=False, show=False,
out_dir=None): out_dir=None,
pipeline=None):
"""Evaluation in KITTI protocol. """Evaluation in KITTI protocol.
Args: Args:
...@@ -237,6 +238,8 @@ class WaymoDataset(KittiDataset): ...@@ -237,6 +238,8 @@ class WaymoDataset(KittiDataset):
Default: False. Default: False.
out_dir (str): Path to save the visualization results. out_dir (str): Path to save the visualization results.
Default: None. Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns: Returns:
dict[str: float]: results of each evaluation metric dict[str: float]: results of each evaluation metric
...@@ -346,7 +349,7 @@ class WaymoDataset(KittiDataset): ...@@ -346,7 +349,7 @@ class WaymoDataset(KittiDataset):
tmp_dir.cleanup() tmp_dir.cleanup()
if show: if show:
self.show(results, out_dir) self.show(results, out_dir, pipeline=pipeline)
return ap_dict return ap_dict
def bbox2result_kitti(self, def bbox2result_kitti(self,
......
tests/test_data/test_datasets/test_kitti_dataset.py
View file @ 78c29c35
...@@ -261,6 +261,30 @@ def test_show(): ...@@ -261,6 +261,30 @@ def test_show():
mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup() tmp_dir.cleanup()
# test show with pipeline
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=classes,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# test multi-modality show # test multi-modality show
tmp_dir = tempfile.TemporaryDirectory() tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name temp_dir = tmp_dir.name
...@@ -283,6 +307,37 @@ def test_show(): ...@@ -283,6 +307,37 @@ def test_show():
mmcv.check_file_exist(img_gt_file) mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup() tmp_dir.cleanup()
# test multi-modality show with pipeline
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4),
dict(type='LoadImageFromFile'),
dict(
type='DefaultFormatBundle3D',
class_names=classes,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
img_file_path = osp.join(temp_dir, '000000', '000000_img.png')
img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png')
img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
def test_format_results(): def test_format_results():
from mmdet3d.core.bbox import LiDARInstance3DBoxes from mmdet3d.core.bbox import LiDARInstance3DBoxes
......
tests/test_data/test_datasets/test_lyft_dataset.py
View file @ 78c29c35
import mmcv import mmcv
import numpy as np import numpy as np
import tempfile
import torch import torch
from mmdet3d.datasets import LyftDataset from mmdet3d.datasets import LyftDataset
...@@ -114,3 +115,54 @@ def test_evaluate(): ...@@ -114,3 +115,54 @@ def test_evaluate():
ap_dict = lyft_dataset.evaluate(results, 'bbox') ap_dict = lyft_dataset.evaluate(results, 'bbox')
car_precision = ap_dict['pts_bbox_Lyft/car_AP'] car_precision = ap_dict['pts_bbox_Lyft/car_AP']
assert car_precision == 0.6 assert car_precision == 0.6
def test_show():
import mmcv
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/lyft'
ann_file = './tests/data/lyft/lyft_infos.pkl'
class_names = ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
'motorcycle', 'bicycle', 'pedestrian', 'animal')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
kitti_dataset = LyftDataset(ann_file, None, root_path)
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [dict(pts_bbox=result)]
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
file_name = 'host-a017_lidar1_1236118886901125926'
pts_file_path = osp.join(temp_dir, file_name, f'{file_name}_points.obj')
gt_file_path = osp.join(temp_dir, file_name, f'{file_name}_gt.obj')
pred_file_path = osp.join(temp_dir, file_name, f'{file_name}_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
tests/test_data/test_datasets/test_nuscene_dataset.py
View file @ 78c29c35
import numpy as np import numpy as np
import tempfile
import torch
from mmdet3d.datasets import NuScenesDataset from mmdet3d.datasets import NuScenesDataset
...@@ -59,3 +61,55 @@ def test_getitem(): ...@@ -59,3 +61,55 @@ def test_getitem():
assert data['img_metas'][0].data['flip'] is False assert data['img_metas'][0].data['flip'] is False
assert data['img_metas'][0].data['pcd_horizontal_flip'] is False assert data['img_metas'][0].data['pcd_horizontal_flip'] is False
assert data['points'][0]._data.shape == (597, 4) assert data['points'][0]._data.shape == (597, 4)
def test_show():
import mmcv
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
nus_dataset = NuScenesDataset('tests/data/nuscenes/nus_info.pkl', None,
'tests/data/nuscenes')
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [dict(pts_bbox=result)]
nus_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
file_name = 'n015-2018-08-02-17-16-37+0800__LIDAR_TOP__1533201470948018'
pts_file_path = osp.join(temp_dir, file_name, f'{file_name}_points.obj')
gt_file_path = osp.join(temp_dir, file_name, f'{file_name}_gt.obj')
pred_file_path = osp.join(temp_dir, file_name, f'{file_name}_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
tests/test_data/test_datasets/test_scannet_dataset.py
View file @ 78c29c35
...@@ -214,6 +214,37 @@ def test_show(): ...@@ -214,6 +214,37 @@ def test_show():
mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup() tmp_dir.cleanup()
# show function with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_getitem(): def test_seg_getitem():
np.random.seed(0) np.random.seed(0)
...@@ -489,7 +520,33 @@ def test_seg_evaluate(): ...@@ -489,7 +520,33 @@ def test_seg_evaluate():
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1 13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long()) ]).long())
results.append(pred_sem_mask) results.append(pred_sem_mask)
ret_dict = scannet_dataset.evaluate(results)
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39)),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
ret_dict = scannet_dataset.evaluate(results, pipeline=eval_pipeline)
assert abs(ret_dict['miou'] - 0.5308) < 0.01 assert abs(ret_dict['miou'] - 0.5308) < 0.01
assert abs(ret_dict['acc'] - 0.8219) < 0.01 assert abs(ret_dict['acc'] - 0.8219) < 0.01
assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01 assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01
...@@ -525,6 +582,44 @@ def test_seg_show(): ...@@ -525,6 +582,44 @@ def test_seg_show():
mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup() tmp_dir.cleanup()
# test show with pipeline
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39)),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_format_results(): def test_seg_format_results():
...@@ -557,4 +652,3 @@ def test_seg_format_results(): ...@@ -557,4 +652,3 @@ def test_seg_format_results():
expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt') expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt')
assert np.all(result_files[0]['seg_mask'] == expected_label) assert np.all(result_files[0]['seg_mask'] == expected_label)
mmcv.check_file_exist(expected_txt_path) mmcv.check_file_exist(expected_txt_path)
tmp_dir.cleanup()
tests/test_data/test_datasets/test_sunrgbd_dataset.py
View file @ 78c29c35
...@@ -210,7 +210,7 @@ def test_show(): ...@@ -210,7 +210,7 @@ def test_show():
from mmdet3d.core.bbox import DepthInstance3DBoxes from mmdet3d.core.bbox import DepthInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory() tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name temp_dir = tmp_dir.name
root_path, ann_file, _, pipelines, modality = \ root_path, ann_file, class_names, pipelines, modality = \
_generate_sunrgbd_dataset_config() _generate_sunrgbd_dataset_config()
sunrgbd_dataset = SUNRGBDDataset( sunrgbd_dataset = SUNRGBDDataset(
root_path, ann_file, pipelines, modality=modality) root_path, ann_file, pipelines, modality=modality)
...@@ -235,14 +235,71 @@ def test_show(): ...@@ -235,14 +235,71 @@ def test_show():
mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup() tmp_dir.cleanup()
# test show with pipeline
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
sunrgbd_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# test multi-modality show # test multi-modality show
tmp_dir = tempfile.TemporaryDirectory() tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name temp_dir = tmp_dir.name
root_path, ann_file, _, multi_modality_pipelines, modality = \ root_path, ann_file, class_names, multi_modality_pipelines, modality = \
_generate_sunrgbd_multi_modality_dataset_config() _generate_sunrgbd_multi_modality_dataset_config()
sunrgbd_dataset = SUNRGBDDataset( sunrgbd_dataset = SUNRGBDDataset(
root_path, ann_file, multi_modality_pipelines, modality=modality) root_path, ann_file, multi_modality_pipelines, modality=modality)
sunrgbd_dataset.show(results, temp_dir, show=False) sunrgbd_dataset.show(results, temp_dir, False, multi_modality_pipelines)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
img_file_path = osp.join(temp_dir, '000001', '000001_img.png')
img_pred_path = osp.join(temp_dir, '000001', '000001_pred.png')
img_gt_file = osp.join(temp_dir, '000001', '000001_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
# test multi-modality show with pipeline
eval_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img', 'calib'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
sunrgbd_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj') pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj') gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj') pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
......
tools/misc/visualize_results.py
View file @ 78c29c35
...@@ -32,7 +32,12 @@ def main(): ...@@ -32,7 +32,12 @@ def main():
results = mmcv.load(args.result) results = mmcv.load(args.result)
if getattr(dataset, 'show', None) is not None: if getattr(dataset, 'show', None) is not None:
dataset.show(results, args.show_dir) # data loading pipeline for showing
eval_pipeline = cfg.get('eval_pipeline', {})
if eval_pipeline:
dataset.show(results, args.show_dir, pipeline=eval_pipeline)
else:
dataset.show(results, args.show_dir) # use default pipeline
else: else:
raise NotImplementedError( raise NotImplementedError(
'Show is not implemented for dataset {}!'.format( 'Show is not implemented for dataset {}!'.format(
......
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